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Record W2066509615 · doi:10.1002/ajim.22003

The relationship between fatigue‐related factors and work‐related injuries in the saskatchewan farm injury Cohort Study

2011· article· en· W2066509615 on OpenAlexafffundabout
Rebbecca Lilley, Lesley Day, Niels Koehncke, James A. Dosman, Louise Hagel, William Pickett

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsCrown Investments Corporation (Canada)Queen's UniversityUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchOxford Academic Health Science Network
KeywordsMedicineLogistic regressionCohortConfoundingOccupational injuryOddsOdds ratioOccupational safety and healthCohort studyInjury preventionPoison controlEnvironmental healthOccupational medicineDemographyOccupational exposureInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The objective was to examine the relationship between seasonal variations in sleep quantity and work-related injuries on Saskatchewan farms. METHODS: A cross-sectional analysis of data from the Saskatchewan Farm Injury Cohort Study was conducted. Analyses were restricted to workers, aged ≥16 years. The primary outcome was work-related injury in the last year. Logistic regression models were used to identify associations between sleep quantity and farm injury. RESULTS: After controlling for confounding variables peak production season sleep was not associated with increased odds of injury. However, those obtaining ≤5 hr sleep per night during non-peak production seasons had increased odds of injury (OR 2.42, 95% CI 1.04-5.59) compared with those sleeping ≥7 hr per night. CONCLUSIONS: We identified that restricted sleep durations, in certain seasons, placed farmers, and farm workers at risk of injury. Agricultural injury intervention programs need to consider the role of seasonal-related variations in sleep on farm injury.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.074
GPT teacher head0.275
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations38
Published2011
Admission routes3
Has abstractyes

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